SoroushVahidi/scidocs huggingface dataset:
Highly sensitive sensor for detection of initial slip and its application in a multi-fingered robot hand
(2011)

Tactile sensors for slip detection are essential for implementing human-like gripping in a robot hand. In previous studies, we proposed flexible, thin and lightweight slip detection sensors utilizing the characteristics of pressure-sensitive conductive rubber. This was achieved by using the high-frequency vibration component generated in the process of slipping of the gripped object in order to distinguish between slipping of the object and changes in the normal force. In this paper, we design a slip detection sensor for a multi-fingered robot hand and examine the influence of noise caused by the operation of such a hand. Finally, we describe an experiment focusing on the adjustment of the gripping force of a multi-fingered robot hand equipped with the developed sensors
Multi-Information Source Optimization
(2017)

We consider Bayesian methods for multi-information source optimization (MISO), in which we seek to optimize an expensive-to-evaluate black-box objective function while also accessing cheaper but biased and noisy approximations (“information sources”). We present a novel algorithm that outperforms the state of the art for this problem by using a Gaussian process covariance kernel better suited to MISO than those used by previous approaches, and an acquisition function based on a one-step optimality analysis supported by efficient parallelization. We also provide a novel technique to guarantee the asymptotic quality of the solution provided by this algorithm. Experimental evaluations demonstrate that this algorithm consistently finds designs of higher value at less cost than previous approaches.
Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion.
(2001)

We used positron emission tomography to study neural mechanisms underlying intensely pleasant emotional responses to music. Cerebral blood flow changes were measured in response to subject-selected music that elicited the highly pleasurable experience of "shivers-down-the-spine" or "chills." Subjective reports of chills were accompanied by changes in heart rate, electromyogram, and respiration. As intensity of these chills increased, cerebral blood flow increases and decreases were observed in brain regions thought to be involved in reward/motivation, emotion, and arousal, including ventral striatum, midbrain, amygdala, orbitofrontal cortex, and ventral medial prefrontal cortex. These brain structures are known to be active in response to other euphoria-inducing stimuli, such as food, sex, and drugs of abuse. This finding links music with biologically relevant, survival-related stimuli via their common recruitment of brain circuitry involved in pleasure and reward.
SPECTRE : Serialization of Proof-of-work Events : Confirming Transactions via Recursive Elections
(2017)

Bitcoin utilizes Nakamoto Consensus to achieve agreement on a consistent set of transactions, in the permissionless setting, where anyone can participate in the protocol anonymously. Since its rise, many other permissionless consensus protocols have been proposed. We present SPECTRE, a new protocol for the consensus core of cryptocurrencies that remains secure even under high throughput and fast confirmation times. At any throughput, SPECTRE is resilient to attackers with up to 50% of the computational power (reaching the limit defined by network congestion and bandwidth constraints). SPECTRE can operate at arbitrarily high block creation rates, which implies that its transactions confirm in mere seconds (limited mostly by the round-trip-time in the network). SPECTRE’s underlying model falls into the category of partial synchronous networks: its security depends on the existence of some bound on the delivery time of messages between honest participants, but the protocol itself does not contain any parameter that depends on this bound. Hence, while other protocols that do encode such parameters must operate with extreme safety margins, SPECTRE converges according to the actual network delay. Key to SPECTRE’s achievements is the fact that it satisfies weaker properties than classic consensus requires. In the conventional paradigm, the order between any two transactions must be decided and agreed upon by all non-corrupt nodes. In contrast, SPECTRE only satisfies this with respect to transactions performed by honest users. We observe that in the context of money, two conflicting payments that are published concurrently could only have been created by a dishonest user, hence we can afford to delay the acceptance of such transactions without harming the usability of the system. Our framework formalizes this weaker set of requirements for a cryptocurrency’s distributed ledger. We then provide a formal proof that SPECTRE satisfies these requirements.
1 Marine anthropogenic litter on British beaches : a 10-year nationwide assessment using citizen science
(2016)

Growing evidence suggests that anthropogenic litter, particularly plastic, represents a highly pervasive and persistent threat to global marine ecosystems. Multinational research is progressing to characterise its sources, distribution and abundance so that interventions aimed at reducing future inputs and clearing extant litter can be developed. Citizen science projects, whereby members of the public gather information, offer a low-cost method of collecting large volumes of data with considerable temporal and spatial coverage. Furthermore, such projects raise awareness of environmental issues and can lead to positive changes in behaviours and attitudes. We present data collected over a decade (2005-2014 inclusive) by Marine Conservation Society (MCS) volunteers during beach litter surveys carried along the British coastline, with the aim of increasing knowledge on the composition, spatial distribution and temporal trends of coastal debris. Unlike many citizen science projects, the MCS beach litter survey programme gathers information on the number of volunteers, duration of surveys and distances covered. This comprehensive information provides an opportunity to standardise data for variation in sampling effort among surveys, enhancing the value of outputs and robustness of findings. We found that plastic is the main constituent of anthropogenic litter on British beaches and the majority of traceable items originate from land-based sources, such as public littering. We identify the coast of the Western English Channel and Celtic Sea as experiencing the highest relative litter levels. Increasing trends over the 10-year time period were detected for a number of individual item categories, yet no statistically significant change in total (effort-corrected) litter was detected. We discuss the limitations of the dataset and make recommendations for future work. The study demonstrates the value of citizen science data in providing insights that would otherwise not be possible due to logistical and financial constraints of running government-funded sampling programmes on such large scales.
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
(2016)

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
Effective Design Parameters on the End Effect in Single-Sided Linear Induction Motors
(2012)

Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model. Keywords—Linear induction motor, end effect, equivalent circuit model, finite-element method.
In-Cell Self-Capacitive-Type Mobile Touch System and Embedded Readout Circuit in Display Driver IC
(2016)

A mobile display system, which includes a self- capacitive type in-cell touch screen panel and a display driver IC, in which the readout circuit is embedded, is developed for reduced cost, improved display performance, and superior touch performance. A touch electrode is implemented with a segmented common electrode of an LCD display. The display system of the in-cell touch panel is 5.7-in a-Si HD (720 × 1280), the number of touch electrodes is 576 (18 × 32), and the sensor pitch is approximately 4 mm. Compared to the display-only system, display performance degradation does not occur in the proposed system since in-cell technology is adopted. Despite using a self-capacitance sensing method in the panel, unlimited multitouch is available by using an all-point sensing scheme; this system shows a touch accuracy of less than 0.2 mm and signal-to-noise ratio is measured to be more than 48 dB in the entire panel without any digital filter. To enhance the touch performance, we proposed noise reduction techniques that are strongly related to the display operation, such as base line compensation, delta adding correlated double sampling, and the use of a high-voltage charge compensator. The power consumption including micro controller unit (MCU) is less than 30 mW.
LinkedIn skills: large-scale topic extraction and inference
(2014)

"Skills and Expertise" is a data-driven feature on LinkedIn, the world's largest professional online social network, which allows members to tag themselves with topics representing their areas of expertise. In this work, we present our experiences developing this large-scale topic extraction pipeline, which includes constructing a folksonomy of skills and expertise and implementing an inference and recommender system for skills. We also discuss a consequent set of applications, such as Endorsements, which allows members to tag themselves with topics representing their areas of expertise and for their connections to provide social proof, via an "endorse" action, of that member's competence in that topic.
Aggregates Caching in Columnar In-Memory Databases
(2013)

The mixed database workloads found in enterprise applications are comprised of short-running transactional as well as analytical queries with resource-intensive data aggregations. In this context, caching the query results of long-running queries is desirable as it increases the overall performance. However, traditional caching approaches are inefficient in a way that changes in the base data result in invalidation or recalculation of cached results. Columnar in-memory databases with a main-delta architecture are optimized for a novel caching mechanism for aggregate queries that is the main contribution of this paper. With the separation into a readoptimized main storage and write-optimized delta storage, we do not invalidate cached query results when new data is inserted to the delta storage. Instead, we use the cached query result and combine it with the newly added records in the delta storage. We evaluate this caching mechanism with mixed database workloads and show how it compares to existing work in this area.
Computer-assisted pop-up design for children: computationally enriched paper engineering
(2006)

Computationally-enriched crafts are activities that blend the advantages of computational media with the affective, social, and cognitive affordances of children's crafts. In this paper, we describe a design application, Popup Workshop, whose purpose is to introduce children to the craft (and engineering discipline) of pop-up design in paper. We describe the fundamental ideas behind computational crafts in general, present our application in its current implementation and offer a scenario for its use, explore the particular ways in which pop-up design serves as fertile ground for integrating computation and tangible design, and discuss our early pilot-tests with elementary-school children, as well as ongoing and related work.
Original Loop-Closure Detection Algorithm for Monocular vSLAM
(2017)

Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays. In this work we study one of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure detection. We analyze the existing methods and propose original algorithm for loop-closure detection, which is suitable for dense, semi-dense and feature-based vSLAM methods. We evaluate the algorithm experimentally and show that it contribute to more accurate mapping while speeding up the monocular vSLAM pipeline to the extent the latter can be used in real-time for controlling small multirotor vehicle (drone).
Detection of Subconscious Face Recognition Using Consumer-Grade Brain-Computer Interfaces
(2016)

We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized; and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn’t (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.
Going Wild: Large-Scale Classification of Open DNS Resolvers
(2015)

Since several years, millions of recursive DNS resolvers are-deliberately or not-open to the public. This, however, is counter-intuitive, since the operation of such openly accessible DNS resolvers is necessary in rare cases only. Furthermore, open resolvers enable both amplification DDoS and cache snooping attacks, and can be abused by attackers in multiple other ways. We thus find open recursive DNS resolvers to remain one critical phenomenon on the Internet.
 In this paper, we illuminate this phenomenon by analyzing it from two different angles. On the one hand, we study the landscape of DNS resolvers based on empirical data we collected for over a year. We analyze the changes over time and classify the resolvers according to device type and software version. On the other hand, we take the viewpoint of a client and measure the response authenticity of these resolvers. Besides legitimate redirections (e.g., to captive portals or router login pages), we find millions of resolvers to deliberately manipulate DNS resolutions (i.e., return bogus IP address information). To understand this threat in more detail, we systematically analyze non-legitimate DNS responses and reveal open DNS resolvers that manipulate DNS resolutions to censor communication channels, inject advertisements, serve malicious files, perform phishing, or redirect to other kinds of suspicious or malicious activities.
System-level Max POwer (SYMPO) - a systematic approach for escalating system-level power consumption using synthetic benchmarks
(2010)

To effectively design a computer system for the worst case power consumption scenario, system architects often use hand-crafted maximum power consuming benchmarks at the assembly language level. These stressmarks, also called power viruses, are very tedious to generate and require significant domain knowledge. In this paper, we propose SYMPO, an automatic SYstem level Max POwer virus generation framework, which maximizes the power consumption of the CPU and the memory system using genetic algorithm and an abstract workload generation framework. For a set of three ISAs, we show the efficacy of the power viruses generated using SYMPO by comparing the power consumption with that of MPrime torture test, which is widely used by industry to test system stability. Our results show that the usage of SYMPO results in the generation of power viruses that consume 14-41% more power compared to MPrime on SPARC ISA. The genetic algorithm achieved this result in about 70 to 90 generations in 11 to 15 hours when using a full system simulator. We also show that the power viruses generated in the Alpha ISA consume 9-24% more power compared to the previous approach of stressmark generation. We measure and provide the power consumption of these benchmarks on hardware by instrumenting a quad-core AMD Phenom II X4 system. The SYMPO power virus consumes more power compared to various industry grade power viruses on x86 hardware. We also provide a microarchitecture independent characterization of various industry standard power viruses.
Socially Aware Conference Participant Recommendation With Personality Traits
(2017)

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and have not gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the aforementioned recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant data set. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.
Planar MIMO antennas for IoT and CR applications
(2017)

In this paper, a planar, compact, single-substrate, multiband 2 sets of 2-elements each multiple-input-multiple-output (MIMO) antenna system is presented. The MIMO antenna system consists of a tunable 2-element meandered and folded MIMO antenna to cover the LTE Band (698 MHz–813 MHz) and a compact 2-element modified truncated cube wideband antenna to cover 754 MHz–971 MHz, 1.65–1.83 GHz and 2–3.66 GHz, respectively. The ground plane of this antenna behaves as a sensing antenna operating in 0.76–1.92 GHz, and 3.0–5.2 GHz. The upper band antennas operate in 0.728–1.08 GHz, 1.64–1.84 GHz, 2.1–3.69 GHz, and 5.01–5.55 GHz range to develop a complete antenna platform for cognitive radios (CR) and Internet of Things (IoT) applications. The antenna is fabricated on a low cost FR-4 substrate (ε<inf>r</inf>=4.4 tanδ=0.02) of dimensions 65 ×120 ×1.56 mm<sup>3</sup>.
Learning Latent Personas of Film Characters
(2013)

We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.
A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance
(2012)

The performance of a photovoltaic (PV) module is mostly affected by array configuration, irradiance, and module temperature. It is important to understand the relationship between these effects and the output power of the PV array. This paper presents a MATLAB-Simulink-based PV module model which includes a controlled current source and an S-Function builder. The modeling scheme in S-Function builder is deduced by some predigested functions. Under the conditions of nonuniform irradiance, the model is practically validated by using different array configurations in testing platform. The comparison experiments indicate that I-V and P-V characteristic curves of simulation match the measurements from outdoor experiment well. Under the conditions of nonuniform irradiance, both simulation and experiment show that the output power of a PV array gets more complicated due to multiple peaks. Moreover, the proposed model can also simulate electric circuit and its maximum power point tracking (MPPT) in the environment of MATLAB-Simulink. The experiments show that the proposed model has good predictability in the general behaviors of MPPT under the conditions of both nonuniform and uniform irradiance.
QuickCut: An Interactive Tool for Editing Narrated Video
(2016)

We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.